Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction

In this paper, we reinterpret error-correcting output codes (ECOCs) as a framework for converting multi-class classification problems into multi-label prediction problems. Different well-known multi-label learning approaches can be mapped upon particular ways of dealing with the original multi-class problem. For example, the label powerset approach obviously constitutes the inverse transformation from multi-label back to multi-class, whereas binary relevance learning may be viewed as the conventional way of dealing with ECOCs, in which each classifier is learned independently of the others. Consequently, we evaluate whether alternative choices for solving the multi-label problem may result in improved performance. This question is interesting because it is not clear whether approaches that do not treat the bits of the code words independently have sufficient error-correcting properties. Our results indicate that a slight but consistent advantage can be obtained with the use of multi-label methods, in particular when longer codes are employed